We used three datasets in the experiments: CIFAR-10, CIFAR-100, and ImageNet [1]}. The CIFAR-10 dataset contains 50K training images and 10K testing images, from 10 classes (the number of images in each class is equal). Following [2]}, we split the original 50K training set into a new 25K training set and a 25K validation set. In the sequel, when we mention “training set", it always refers to the new 25K training set. During architecture search, the training set is used as the training data \(D_{ee}^{(\textrm {tr})}\)  of the learner and the training data \(D_{er}^{(\textrm {tr})}\)  of the tester. The validation set is used as the test bank \(D_b\)  and the validation data \(D_{er}^{(\textrm {val})}\)  of the tester. During architecture evaluation, the combination of the training data and validation data is used to train the large network stacking multiple copies of the searched cell. The CIFAR-100 dataset contains 50K training images and 10K testing images, from 100 classes (the number of images in each class is equal). Similar to CIFAR-100, the 50K training images are split into a 25K training set and 25K validation set. The usage of the new training set and validation set is the same as that for CIFAR-10. The ImageNet dataset contains a training set of 1.2M images and a validation set of 50K images, from 1000 object classes. The validation set is used as a test set for architecture evaluation.
Following [2]}, we evaluate the architectures searched using CIFAR-10 and CIFAR-100 on ImageNet: given a cell searched using CIFAR-10 and CIFAR-100, multiple copies of it compose a large network, which is then trained on the 1.2M training data of ImageNet and evaluated on the 50K test data.
